ArticleThe neuroradiology journal2018
Volumetric voxelwise apparent diffusion coefficient histogram analysis for differentiation of the fourth ventricular tumors.
Article in The neuroradiology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review.Neurosurgical review · 2024Pooled it
- Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence.Journal of cancer research and clinical oncology · 2023Article
- Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper.Pediatric blood & cancer · 2023Article
- The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups.Pediatric radiology · 2022Article
- The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.Frontiers in oncology · 2020Article
- Comparison of free breathing and respiratory triggered diffusion-weighted imaging sequences for liver imaging.World journal of radiology · 2019Article
- Histogram Analysis Parameters ADC for Distinguishing Ventricular Neoplasms of Ependymoma, Choroid Plexus Papilloma, and Central Neurocytoma.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
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Abstract
purposeWe applied voxelwise apparent diffusion coefficient (ADC) histogram analysis in addition to structural magnetic resonance imaging (MRI) findings and patients' age for differentiation of intraaxial posterior fossa tumors involving the fourth ventricle. PARTICIPANTS AND
methodsPretreatment MRIs of 74 patients with intraaxial brain neoplasm involving the fourth ventricle, from January 1, 2004 to December 31, 2015, were reviewed. The tumor solid components were segmented and voxelwise ADC histogram variables were determined. Histogram-driven variables, structural MRI findings, and patient age were combined to devise a differential diagnosis algorithm.
resultsThe most common neoplasms were ependymomas ( n = 21), medulloblastoma ( n = 17), and pilocytic astrocytomas ( n = 13). Medulloblastomas followed by atypical teratoid/rhabdoid tumors had the lowest ADC histogram percentile values; whereas pilocytic astrocytomas and choroid plexus papillomas had the highest ADC histogram percentile values. In a multivariable multinominal regression analysis, the ADC 10th percentile value from voxelwise histogram was the only independent predictor of tumor type ( p < 0.001). In separate binary logistic regression analyses, the 10th percentile ADC value, tumor morphology, enhancement pattern, extension into Luschka/Magendie foramina, and patient age were predictors of different tumor types. Combining these variables, we devised a stepwise diagnostic model yielding 71% to 82% sensitivity, 91% to 95% specificity, 75% to 78% positive predictive value, and 89% to 95% negative predictive value for differentiation of ependymoma, medulloblastoma, and pilocytic astrocytoma.
conclusionWe have shown how the addition of quantitative voxelwise ADC histogram analysis of the tumor solid component to structural findings and patient age can help with accurate differentiation of intraaxial posterior fossa neoplasms involving the fourth ventricle based on pretreatment MRI.
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